BEV Semantic Segmentation.
BEV segmentation methods [1]}, [2]} attempt to predict BEV semantic maps of 3D scenes from images. Images can be used to either directly estimate BEV semantic maps [3]}, [4]}, [5]} or to estimate a BEV feature representation [6]}, [7]}, [8]} as an intermediate step for the segmentation task. In particular, Lift, Splat, Shoot [6]} predicts categorical depth distributions in an unsupervised manner, in order to generate intermediate BEV representations. In this work, we predict categorical depth distributions via supervision with ground truth one-hot encodings to generate more accurate depth distributions for object detection.
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